1. Field of the Invention
The present invention generally relates to an apparatus and a method for regulating a signal strength of a wireless positioning system, and more particularly, to a method and an apparatus for regulating a signal strength of a wireless positioning system, adapted for eliminating a signal strength variation caused by using different wireless devices, network cards, or in different environments.
2. Description of Related Art
Currently, there are many positioning technologies developed and used in navigation, vehicle, and rescue, etc. Recent development of wireless technologies allows the positioning technologies to achieve positioning with signal strength of wireless electric waves. Generally, a strength distribution of wireless signals received by a wireless mobile apparatus can be taken to compare with a signal strength distribution model and then calculated with a fingerprinting method to obtain a possible position probability distribution.
FIG. 1 is a schematic diagram illustrating a system structure of a wireless positioning system 10 and a positioning method thereof. Referring to FIG. 1, the wireless positioning system 10 includes a plurality of wireless access points 100 through 102, and a mobile communication apparatus 110. The mobile communication apparatus 110 would communicate with a wireless positioning apparatus 111. The wireless positioning apparatus 111 is provided to perform an operation flow from steps S120 to S122 for positioning which can be embedded in mobile communication apparatus 110 or in another apparatus.
At step S120, the mobile communication apparatus 110 collects wireless signals emitted from the wireless access points 100 through 102, and the wireless positioning apparatus 111 calculates the wireless signals emitted from the wireless access points 100 through 102 to obtain a signal strength distribution of the collected wireless signals. At step S121, the signal strength distribution of the collected wireless signals is compared with a signal strength distribution model, and a possible position probability distribution is obtained by calculating with a signal fingerprinting method. The signal strength distribution model is a signal strength distribution pre-collected by the mobile communication apparatus 110 from the entire space. Finally, at step S122, a location of the mobile communication apparatus 110 is determined according to the possible position probability distribution obtained from step S121.
Further, the wireless positioning technology may use a hidden Markov model (HMM) to further improve the positioning accuracy. First, a previous possible position probability distribution (also known as prior probability distribution), is relied upon to estimate a present possible position probability distribution, and received signal strength is used for calculating probability distribution by fingerprint. Then final possible position probability distribution (also known as posterior possible position probability distribution) is computed by consider former two probability distribution.
FIG. 2 is a schematic diagram illustrating a concept of a positioning method of calculating the final possible position probability distribution according to an HMM 20. Referring to FIG. 2, the HMM 20 includes an prior possible position probability distribution L(t−1), L(t), L(t+1), possible position probability distribution computed by fingerprint O(Ot−1), O(Ot), O(Ot+1), and the posterior possible position probability distribution, P(t−1), P(t), P(t+1). Wherein the posterior possible position probability distribution P(t−1) represents a posterior probability distribution of the position probability distribution at (t−1) second; L(t), the prior probability distribution at t second, can be obtained according to the posterior probability distribution P(t−1) by considering the possible motion of the human being. That is if the probability the probability distribution P(t−1) at a specific position is higher, the probability of the prior probability distribution L(t) will be higher adjacent to the specific position.
The signal strength distribution Ot collected by the mobile apparatus at each second is an independent event, and is related to the position thereof, and is not affected by the signal strength distribution of the previous second. Further, the post-calculation possible position probability distribution O(Ot) is obtained by putting the collected signal strength distribution Ot into a signal strength distribution probability model and calculating with a signal fingerprinting method. The posterior possible position probability distribution P(t) can be obtained by calculating according to the prior possible position probability distribution L(t), and the post-calculation possible position probability distribution O(Ot), so as to determine a final position.
The posterior possible position probability distribution P(t) may affect the anterior possible position probability distribution L(t+1). As shown in FIG. 2, the anterior possible position probability distribution L(t+1) is obtained by a Markov calculation conducted to the posterior possible position probability distribution P(t). Because there are many positions having a similar signal strength distribution distributed in the space, when the wireless positioning technology is applied, the HMM is helpful to eliminate some positions of lower probabilities, and thus improving the accuracy of the positioning.
The collected signal strength distribution Ot is a critical factor affecting the positioning. If the chip model of the mobile communication apparatus used for positioning differs from the chip model of the mobile communication apparatus used for collecting data of signal strength distribution model, signal strengths collected by mobile communication apparatuses with different chip may be different. As such, the signal fingerprinting method may cause an error in positioning, and thus lowering the positioning accuracy. Further, a current wireless fidelity (WiFi) positioning technology typically adopts the signal fingerprinting method, and requires a signal strength distribution model, thus it must have a specific mobile communication apparatus in advance to collect the signal strength distribution of the entire space.
Supposing that there are k wireless access points; a handheld apparatus H is located in a certain position Li={x,y} in the space; and a quality value distribution of the received signals can be represented as Ds(H,Li). The signal quality value distribution Ds(H,Li) is composed of signal strengths of k wireless access points received by the handheld apparatus H at the certain position Li={x,y}. In such a way, the signal quality value distribution Ds(H,Li) can be represented as Ds(H,Li)={s1, s2, . . . , sk}. Usually, the signal quality value distribution Ds(H,Li) is accessed by signal strengths. As such, a signal quality value sj can be taken as a representation of received signal strength (RSS), so that the signal quality value distribution Ds(H,Li) can be taken as a signal strength distribution.
In an optimal condition, a signal strength distributions received in the same location should be similar. The signal fingerprinting method is to build a signal strength distribution model by collecting or deducing the signal quality value distribution Ds(H,Li) at every position in the space. A signal strength distribution model is a set of signal quality value distributions Ds(H,Li) at all positions in the entire space Li Ds(H,Li), ∀ Li in space. Then a signal strength distribution Ot={s′1, s′2, . . . , s′k} can be compared with the signal quality value distribution Ds(H,Li) to obtain a probability P(Ds(H,Li)|Ot) corresponding to a certain position Li. Further the possible position probability distribution set O(Ot)={P(Ds(H,Li)|Ot), ∀ Li in space} is computed according to the signal strength distribution Ot at a time point t. Then the posterior possible position probability distribution P(t) can be obtained base on the prior possible position probability distribution L(t) and position probability distribution O(Ot). And thereby, the real position can be evaluated by the posterior possible position probability distribution P(t).
The above method is based upon a basic assumption, that is the mobile communication apparatus used for collecting the signal strength distribution model and the mobile communication apparatus used for positioning should be identical. When such an assumption is satisfied, the signal fingerprinting method performs well, and usually performs with an error value less than 3 meters. However, when the mobile communication apparatuses for collecting the signal strength distribution model and for positioning, respectively, are different, the positioning accuracy is drastically decreased.
Supposing a mobile communication apparatus used for collecting the signal strength distribution model is Hc, and a mobile communication apparatus used for positioning is Hx, if the located position is L, there would not be high likelihood between Ds(Hc,L) and Ds(Hx, L). As such, if Ot is the signal strength distribution of the mobile communication apparatus Hx received at the time point t, a relative large error may occur.
Referring to U.S. Publication Application No. 2005/0181804 A1 submitted by Misikangas et al., there is employed a transforming module f so as to allow Ds(Hc,L) and Ds(Hx,L) to have a better likelihood, that is trying to satisfy the equation Ds(Hc,L)=f(Ds(Hx,L)). FIG. 3 is a system block diagram of a wireless positioning system 30 having a transforming module proposed by Misikangas et al. Referring to FIG. 3, the wireless positioning system 30 includes a plurality of wireless access points 301, 302, . . . , 30k, a plurality of mobile communication apparatuses 311, 312, . . . , 31n, and a transforming and positioning module 320. The transforming and positioning module 320 includes a transforming module circuit 321, a position calculation module 322, a selecting circuit 323, and a look-up table 324.
The mobile communication apparatuses 311, 312, . . . , 31n, are different mobile communication apparatuses, respectively corresponding to models H1, H2, . . . , Hn. Data modules DM1, DM2, . . . , DMn in the look-up table 324 are transforming modules of the signal strength distribution received by different mobile communication apparatuses 311, 312, . . . , 31n. The transforming module circuit 321 includes a plurality of transforming modules DM′1, DM′2, . . . , DM′n, for transforming the received signal strength distributions. The position calculation module 322 is provided for calculating the position according to the transformed signal strength distribution, so as to determine the position. The selecting module 323 is adapted to select a corresponding transforming module DMi from the look-up table 324 according to the model of the mobile communication apparatus, and thus determining a transforming module DM′i in the transforming module circuit 321 to use according to the corresponding transforming module DMi. This is a relatively simple approach, in which a transforming module is built corresponding to all mobile communication apparatuses 311 through 31n. Prior to calculating the position, the corresponding transforming module is selected from the look-up table 324 according to the model of the mobile communication apparatus, and then the received signal strength is transformed by the selected transforming module to a corresponding value of the signal strength distribution model received by the apparatus. The transformed value is then introduced into the above calculation to obtain the position.
Although relatively simple, this approach is time-consuming to generate a large look-up table 324, in which all possibilities of matching must be considered. Further, the wireless positioning system must be aware of the models of the currently used mobile communication apparatuses 311 through 3 in, before looking up the look-up table 324. However, sometimes the models of the currently used mobile communication apparatuses 311 through 31n cannot be obtained by software or the operation systems. Or otherwise, even when the models are obtained, the models may not correspond to the contents in the look-up table 324.
Correspondingly, the U.S. Patent Application No. 2005/0181804 A1 proposes a substitutive approach, in which if accurate models H1 through Hn cannot be obtained, or the models H1 through Hn are not well matched, some locations which are convenient for judgement are provided to serve as reference points, for example, an inlet of the space. Therefore, the transforming and positioning module 320 may probably determine that the user happens to be at the position of the inlet of the space, and then automatically determine a transforming module DMi of an optimal calculation result from the look-up table 324.
Referring now to “Practical robust localization over large-scale 802.11 wireless networks,” A. Haeberlen, E. Flannery, A. M. Ladd, A. Rudys, D. S. Wallach, and L. E. Kavraki, published in Proceedings of ACM MOBICOM, 2004. Haeberalen et al. consider the problem to be similar to that described by Misikangas et al., while providing more practical examples and more detailed discussion of such practical examples, and therefore the thesis is more valuable for reference.
FIG. 4 is a table showing three different sets of WiFi chip models and the relative equations thereof. FIG. 5 is a corresponding curve diagram showing practically transformed signal strengths and experimental signal strengths received by different mobile communication apparatuses. Referring to FIG. 4, the table is practically obtained from an experiment of Heaberlen et al. Corresponding to different chip models, C1 and C2 can be learnt from the correlation which are practically transformed and practically experimented. Referring to FIG. 5, x-axis represents the signal strength of the signal strength distribution received by the mobile communication apparatus Hc, and y-axis represents the signal strength of the signals strength distribution received by an unknown apparatus Hx. As shown in FIG. 5, the points marked with X correspond to signal strengths which are not transformed, while the points marked with ∘ correspond to the linearly transformed signal strengths. Apparently, the points marked with ∘ are very close to the ideal curve, and therefore the linear transformation proposed by Haeberlen et al. is rather successful.
In the thesis, Haeberlen et al. further discuss an interesting fact, that is the changes of the signal strengths caused by the environmental variation are also linear. FIG. 6 is a corresponding curve diagram showing practically transformed signal strengths and experimental signal strengths received in different environments. Referring to FIG. 6, x-axis represents the signal strength of the signals strength distribution received in a morning of a certain day, and y-axis represents the signal strength of the signals strength distribution received at the late night of the certain day. Since the actions of the human being between the daytime and the night time are different, and some wireless access points are usually turned off during the night time, the signal strengths are drastically varied. The points marked with X marks corresponds to signal strengths which are not transformed, while the points marked with ∘ marks corresponds to linearly transformed signal strengths. Apparently, the points marked with ∘ marks are very close to the ideal curve, and therefore the linear transformation proposed by Haeberlen et al. is rather successful.
As such, a better accuracy can be achieved by incorporating a pre-built transforming module with the linear correlation proposed by Haeberlen et al. However, as specified in the thesis, the transforming module must be built in a manual or semi-manual manner. Although, Haeberlen et al. proposes to use expectation maximization for automatically training the transforming module, Haeberlen et al. admit that the corresponding positioning performance is not as good as the manual method. Further, Haeberlen et al. fail to teach the procedure and the step of the expectation maximization method.
In summary, although all of the above-mentioned methods provide a solution to the problem that different mobile communication apparatuses are used when training and positioning in the wireless positioning system, each of the methods has to previously determine the matching linear correlation. Since there are not many WiFi chip manufacturers, and it might be possible to determine matching relationship between the existing chip models. In addition, if the same chips model are equipped to different mobile communication apparatuses, they may be operated with different antennas. For example, laptop computers from different manufacturers may adopt same chip models, but unfortunately, the antennas equipped thereto are very likely to be different, and thus generate signal variations. In this manner, it is almost a mission impossible to provide transformation modules for matching all kinds of mobile communication apparatuses, while regularly updating data in the look-up table. On the other hand, the automatic method proposed by Haeberlen et al. does not provide any detail of feasible solution thereto.